Online support vector machine based on linear independent

Author

Chih-Chia Yao ; Jun-An Chen ; Yu-Siang Houng

Author_Institution

Comput. Sci. & Inf. Eng., Chaoyang Univ. of Technol., Taichung, Taiwan

Volume

3

fYear

2014

fDate

26-28 April 2014

Firstpage

1470

Lastpage

1474

Abstract

In this paper a novel online learning algorithm for support vector machine is proposed. In this algorithm support vector are extracted based on the property of linear independent. Error estimation can be quickly got by using least square support vector machine. Then learning scheme is executed or not depended on the classification error. In the learning scheme support vectors are re-selected and re-training according to linear independent and WLS-SVM. Experimental results reveal that our algorithm outperforms IncrSVM and LIBSVM on the time complexity and classification rate.

Keywords

Internet; learning (artificial intelligence); support vector machines; IncrSVM; LIBSVM; WLS-SVM; algorithm support vector; classification error; classification rate; error estimation; learning scheme support vectors; least square support vector machine; online learning algorithm; online support vector machine; time complexity; Classification algorithms; Databases; Error analysis; Kernel; Least squares approximations; Support vector machines; Training; linear independent; online learning; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Science, Electronics and Electrical Engineering (ISEEE), 2014 International Conference on

Conference_Location

Sapporo

Print_ISBN

978-1-4799-3196-5

Type

conf

DOI

10.1109/InfoSEEE.2014.6946164

Filename

6946164